太空望远镜观测调度优化研究进展  

Research Progress in Space Telescope Observation Scheduling

作　　者：张开元 夏小云 张先超 江海[4] 刘静[4] 廖伟志 ZHANG Kai-yuan;XIA Xiao-yun;ZHANG Xian-chao;JIANG Hai;LIU Jing;LIAO Wei-zhi(School of Sciences,Jiangxi University of Science and Technology,Ganzhou 341000,China;School of Information Science and Engineering,Jiaxing University,Jiaxing 314001,China;Institute of Information Network and Artificial Intelligence,Jiaxing University,Jiaxing 314001,China;National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100012,China)

机构地区：[1]江西理工大学理学院,赣州341000 [2]嘉兴学院信息科学与工程学院,嘉兴314001 [3]嘉兴学院信息网络与智能研究院,嘉兴314001 [4]中国科学院国家天文台,北京100012

出　　处：《空间碎片研究》2023年第4期22-35,共14页Space Debris Research

基　　金：空间碎片与近地小行星防御专项科研项目(KJSP2020020202);国家自然科学基金(61703183);浙江省自然科学基金(LGG19F030010)。

摘　　要：随着人类对太空的持续探索,空间碎片数量不断增加,对空间碎片的观测需求也逐步增长。太空望远镜作为空间目标观测的重要设备,其性能需求也不断提高,使得观测调度变得更加困难。首先,本文列出了太空望远镜调度问题的几种典型模型和每种模型的常用求解算法,同时介绍了不同应用场景下的望远镜调度约束条件和优化目标,并对每种模型的常用算法进行了优劣分析。其次,阐述了国内外太空望远镜调度系统的发展过程及最新研究进展。最后,根据望远镜调度的研究现状,对太空望远镜调度的未来发展趋势进行展望。With the continuous exploration of space by humans,the number of space debris is constantly increasing,and the demand for observation of space debris is also gradually increasing.As essential equipment for observing space objects,the performance requirements of space telescopes are constantly improving,making observation scheduling more difficult.Firstly,this article lists several typical models of space telescope scheduling problems and commonly used solving algorithms for each model.At the same time,it introduces telescope scheduling constraints and optimization objectives in different application scenarios and analyzes the advantages and disadvantages of commonly used algorithms for each model.Secondly,the development process and latest research progress of space telescope scheduling systems at home and abroad were elaborated.Finally,based on the current research status of telescope scheduling,prospects are made for the future development trend of space telescope scheduling.

关 键 词：空间碎片观测 太空望远镜调度 组合优化 启发式算法 深度强化学习 

分 类 号：V476[航空宇航科学与技术—飞行器设计]